Incremental Support Vector Machine Learning: A Local Approach. - Liva Ralaivola; Florence d'Alché-Buc (ICANN, 2001)

Gene networks inference using dynamic Bayesian networks. - Bruno-Edouard Perrin; Liva Ralaivola; Aurélien Mazurie; Samuele Bottani; Jacques Mallet; Florence d'Alché-Buc (ECCB, 2003)

Dynamical Modeling with Kernels for Nonlinear Time Series Prediction. - Liva Ralaivola; Florence d'Alché-Buc (NIPS, 2003)

SVM and pattern-enriched common fate graphs for the game of go. - Liva Ralaivola; Lin Wu; Pierre Baldi (ESANN, 2005)

Time series filtering, smoothing and learning using the kernel Kalman filter. - Liva Ralaivola; Florence d'Alché-Buc (IJCNN, 2005)

Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity. - Sanjay Joshua Swamidass; Jonathan H. Chen; Jocelyne Bruand; Peter Phung; Liva Ralaivola; Pierre Baldi (ISMB (Supplement of Bioinformatics), 2005)

Graph kernels for chemical informatics. - Liva Ralaivola; Sanjay Joshua Swamidass; Hiroto Saigo; Pierre Baldi (Neural Networks, 2005)

Efficient learning of Naive Bayes classifiers under class-conditional classification noise. - François Denis; Christophe Nicolas Magnan; Liva Ralaivola (ICML, 2006)

CN = CPCN. - Liva Ralaivola; François Denis; Christophe Nicolas Magnan (ICML, 2006)

The Pharmacophore Kernel for Virtual Screening with Support Vector Machines. - Pierre Mahé; Liva Ralaivola; Véronique Stoven; Jean-Philippe Vert (J. Chem. Inf. Model., 2006)

Learning Kernel Perceptrons on Noisy Data Using Random Projections. - Guillaume Stempfel; Liva Ralaivola (ALT, 2007)

One- to Four-Dimensional Kernels for Virtual Screening and the Prediction of Physical, Chemical, and Biological Properties. - Chloé-Agathe Azencott; Alexandre Ksikes; S. Joshua Swamidass; Jonathan H. Chen; Liva Ralaivola; Pierre Baldi (J. Chem. Inf. Model., 2007)

Semi-supervised bipartite ranking with the normalized Rayleigh coefficient. - Liva Ralaivola (ESANN, 2009)

Learning SVMs from Sloppily Labeled Data. - Guillaume Stempfel; Liva Ralaivola (ICANN (1), 2009)

Grammatical inference as a principal component analysis problem. - Raphaël Bailly; François Denis; Liva Ralaivola (ICML, 2009)

Multiple indefinite kernel learning with mixed norm regularization. - Matthieu Kowalski; Marie Szafranski; Liva Ralaivola (ICML, 2009)

Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary beta-Mixing Processes - Liva Ralaivola; Marie Szafranski; Guillaume Stempfel (CoRR, 2009)

Chromatic PAC-Bayes Bounds for Non-IID Data. - Liva Ralaivola; Marie Szafranski; Guillaume Stempfel (AISTATS, 2009)

Empirical Bernstein Inequalities for U-Statistics. - Thomas Peel; Sandrine Anthoine; Liva Ralaivola (NIPS, 2010)

Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary β-Mixing Processes. - Liva Ralaivola; Marie Szafranski; Guillaume Stempfel (J. Mach. Learn. Res., 2010)

Applying Multiclass Bandit algorithms to call-type classification. - Liva Ralaivola; Benoît Favre; Pierre Gotab; Frédéric Béchet; Géraldine Damnati (ASRU, 2011)

MKPM: A multiclass extension to the kernel projection machine. - Sylvain Takerkart; Liva Ralaivola (CVPR, 2011)

Stochastic Low-Rank Kernel Learning for Regression. - Pierre Machart; Thomas Peel; Sandrine Anthoine; Liva Ralaivola; Hervé Glotin (ICML, 2011)

Matching pursuit with stochastic selection. - Thomas Peel; Valentin Emiya; Liva Ralaivola; Sandrine Anthoine (EUSIPCO, 2012)

PAC-Bayesian Generalization Bound on Confusion Matrix for Multi-Class Classification. - Emilie Morvant; Sokol Koço; Liva Ralaivola (ICML, 2012)

Graph-Based Inter-subject Classification of Local fMRI Patterns. - Sylvain Takerkart; Guillaume Auzias; Bertrand Thirion; Daniele Schön; Liva Ralaivola (MLMI, 2012)

Confusion-Based Online Learning and a Passive-Aggressive Scheme. - Liva Ralaivola (NIPS, 2012)

Confusion Matrix Stability Bounds for Multiclass Classification - Pierre Machart; Liva Ralaivola (CoRR, 2012)

Fast online adaptivity with policy gradient: example of the BCI "P300"-speller. - Emmanuel Daucé; Timothée Proix; Liva Ralaivola (ESANN, 2013)

A dynamic screening principle for the Lasso. - Antoine Bonnefoy; Valentin Emiya; Liva Ralaivola; Rémi Gribonval (EUSIPCO, 2014)

Multiple subject learning for inter-subject prediction. - Sylvain Takerkart; Liva Ralaivola (PRNI, 2014)

Stationary Mixing Bandits. - Julien Audiffren; Liva Ralaivola (CoRR, 2014)

Online multiclass learning with "bandit" feedback under a Passive-Aggressive approach. - Hongliang Zhong; Emmanuel Daucé; Liva Ralaivola (ESANN, 2015)

More efficient sparsity-inducing algorithms using inexact gradient. - Alain Rakotomamonjy; Sokol Koço; Liva Ralaivola (EUSIPCO, 2015)

Entropy-Based Concentration Inequalities for Dependent Variables. - Liva Ralaivola; Massih-Reza Amini (ICML, 2015)

On Binary Reduction of Large-Scale Multiclass Classification Problems. - Bikash Joshi; Massih-Reza Amini; Ioannis Partalas; Liva Ralaivola; Nicolas Usunier; Éric Gaussier (IDA, 2015)

Reward-based online learning in non-stationary environments: Adapting a P300-speller with a "backspace" key. - Emmanuel Daucé; Timothée Proix; Liva Ralaivola (IJCNN, 2015)

From cutting planes algorithms to compression schemes and active learning. - Ugo Louche; Liva Ralaivola (IJCNN, 2015)

Cornering Stationary and Restless Mixing Bandits with Remix-UCB. - Julien Audiffren; Liva Ralaivola (NIPS, 2015)

On Generalizing the C-Bound to the Multiclass and Multi-label Settings. - François Laviolette; Emilie Morvant; Liva Ralaivola; Jean-Francis Roy (CoRR, 2015)

Greedy methods, randomization approaches and multi-arm bandit algorithms for efficient sparsity-constrained optimization. - Alain Rakotomamonjy; Sokol Koço; Liva Ralaivola (CoRR, 2015)

Unconfused ultraconservative multiclass algorithms. - Ugo Louche; Liva Ralaivola (Mach. Learn., 2015)

Dynamic Screening: Accelerating First-Order Algorithms for the Lasso and Group-Lasso. - Antoine Bonnefoy; Valentin Emiya; Liva Ralaivola; Rémi Gribonval (IEEE Trans. Signal Process., 2015)

Indistinguishable Bandits Dueling with Decoys on a Poset. - Julien Audiffren; Liva Ralaivola (CoRR, 2016)

Online Learning of Task-specific Word Representations with a Joint Biconvex Passive-Aggressive Algorithm. - Pascal Denis; Liva Ralaivola (EACL (1), 2017)

Bandits Dueling on Partially Ordered Sets. - Julien Audiffren; Liva Ralaivola (NIPS, 2017)

Risk upper bounds for general ensemble methods with an application to multiclass classification. - François Laviolette; Emilie Morvant; Liva Ralaivola; Jean-Francis Roy (Neurocomputing, 2017)

Greedy Methods, Randomization Approaches, and Multiarm Bandit Algorithms for Efficient Sparsity-Constrained Optimization. - Alain Rakotomamonjy; Sokol Koço; Liva Ralaivola (IEEE Trans. Neural Networks Learn. Syst., 2017)

Frank-Wolfe Algorithm for the Exact Sparse Problem. - Farah Cherfaoui; Valentin Emiya; Liva Ralaivola; Sandrine Anthoine (CoRR, 2018)

Learning Rich Event Representations and Interactions for Temporal Relation Classification. - Onkar Arun Pandit; Pascal Denis; Liva Ralaivola (ESANN, 2019)

QuicK-means: Acceleration of K-means by learning a fast transform. - Luc Giffon; Valentin Emiya; Liva Ralaivola; Hachem Kadri (CoRR, 2019)

Recovery and Convergence Rate of the Frank-Wolfe Algorithm for the m-Exact-Sparse Problem. - Farah Cherfaoui; Valentin Emiya; Liva Ralaivola; Sandrine Anthoine (IEEE Trans. Inf. Theory, 2019)

Partial Trace Regression and Low-Rank Kraus Decomposition. - Hachem Kadri; Stéphane Ayache; Riikka Huusari; Alain Rakotomamonjy; Liva Ralaivola (ICML, 2020)

Quantum bandits. - Balthazar Casalé; Giuseppe Di Molfetta; Hachem Kadri; Liva Ralaivola (Quantum Mach. Intell., 2020)

Differentially Private Sliced Wasserstein Distance. - Alain Rakotomamonjy; Liva Ralaivola (ICML, 2021)

Photonic Differential Privacy with Direct Feedback Alignment. - Ruben Ohana; Hamlet Jesse Medina Ruiz; Julien Launay; Alessandro Cappelli; Iacopo Poli; Liva Ralaivola; Alain Rakotomamonjy (NeurIPS, 2021)

QuicK-means: accelerating inference for K-means by learning fast transforms. - Luc Giffon; Valentin Emiya; Hachem Kadri; Liva Ralaivola (Mach. Learn., 2021)

Scalable Ridge Leverage Score Sampling for the Nyström Method. - Farah Cherfaoui; Hachem Kadri; Liva Ralaivola (ICASSP, 2022)

Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances. - Ruben Ohana; Kimia Nadjahi; Alain Rakotomamonjy; Liva Ralaivola (ICML, 2023)

Federated Wasserstein Distance. - Alain Rakotomamonjy; Kimia Nadjahi; Liva Ralaivola (ICLR, 2024)

Personalised Federated Learning On Heterogeneous Feature Spaces. - Alain Rakotomamonjy; Maxime Vono; Hamlet Jesse Medina Ruiz; Liva Ralaivola (Trans. Mach. Learn. Res., 2024)